Abstract 773: Early detection of ovarian cancer using cell-free DNA fragmentomes
Authors: Akshaya V. Annapragada, Jamie E. Medina, Pien Lof, Dimitrios Mathios, Zachariah H. Foda, Michaël Noë, Sarah Short, Adrianna Bartolomucci, Daniel C. Bruhm, Euihye Jung, Jenna Canzoniero, Noushin Niknafs, Stephen Cristiano, Vilmos Adleff, Heather Symecko, Daan van de Broek, Stephen B. Baylin, Michael F. Press, Dennis Slamon, Gottfried Konecny, Susan Domchek, Ronny Drapkin, Jillian Phallen, Robert B. Scharpf, Christianne Lok, Victor E. Velculescu
Journal: Cancer Research (2023)

Abstract Introduction: Ovarian cancer is the leading cause of death from gynecological related cancers worldwide. Most patients are diagnosed at late stages due to asymptomatic disease and lack of effective screening modalities. Additionally, in women with an ovarian mass the diagnosis of ovarian cancer can be challenging with many women having surgery only to discover a benign pathology. Liquid biopsies, including analyses of cell-free DNA (cfDNA) fragmentomes in the circulation, have shown promise for the early detection of cancer and may provide a useful avenue for detection of ovarian cancer. Methods: To evaluate cfDNA fragmentomes for detecting ovarian cancer, we assessed plasma from 507 women, including 128 with ovarian cancers comprising high grade serous, endometrioid, mucinous and clear cell subtypes, 48 with benign masses, and 223 without cancer, as well as a validation cohort of 108 women with (n=14) and without (n=94) ovarian cancers from a separate institution. We obtained cfDNA from each individual and performed low-coverage (1-2x) whole genome sequencing. The cfDNA fragmentome data were analyzed with our DELFI (DNA evaluation of fragments for early interception) approach optimized for high specificity. We used a cross-validated machine learning model, and the fixed DELFI model was evaluated in the external validation cohort. Results: Individuals with ovarian cancer had significantly higher DELFI scores than those without cancer (mean 0.59 vs 0.18, respectively, p &amp;lt; 0.0001) resulting in an AUC of 0.85 (95% CI = 0.80-0.90). DELFI was successful in identifying high-grade serous ovarian cancer across all stages, with sensitivities of 56%, 60%, 58% and 100% for stages I - IV, respectively, at 99% specificity. We further applied DELFI to evaluate women with ovarian masses in a prospective observational cohort (NL58253.031.16). Women with benign masses had DELFI scores lower than those with ovarian cancer (mean 0.23 vs 0.59, p&amp;lt; .0001) and were distinguished with an overall AUC of 0.80 (95% CI = 0.73-0.86). In the external validation cohort, women with cancer were distinguished from women without cancer with an AUC of 0.88 (95% CI = 0.72 - 1.0). At the DELFI score threshold with 99% specificity in the cross-validated cohort, the external validation cohort had specificity of 97% with an overall sensitivity of 79%. The cfDNA fragmentome profiles reflected chromosomal, chromatin, transcription factor binding site, and disease-specific pathway changes known to be altered in ovarian cancer. We are extending these efforts to over 1000 individuals with and without ovarian cancer and the integrated results will be presented. Conclusion: Overall, we demonstrate the utility of cfDNA fragmentomes for noninvasive detection of ovarian cancer. These results may provide a feasible approach for ovarian cancer screening and management of patients with ovarian masses. Citation Format: Akshaya V. Annapragada, Jamie E. Medina, Pien Lof, Dimitrios Mathios, Zachariah H. Foda, Michaël Noë, Sarah Short, Adrianna Bartolomucci, Daniel C. Bruhm, Euihye Jung, Jenna Canzoniero, Noushin Niknafs, Stephen Cristiano, Vilmos Adleff, Heather Symecko, Daan van de Broek, Stephen B. Baylin, Michael F. Press, Dennis Slamon, Gottfried Konecny, Susan Domchek, Ronny Drapkin, Jillian Phallen, Robert B. Scharpf, Christianne Lok, Victor E. Velculescu. Early detection of ovarian cancer using cell-free DNA fragmentomes [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 773.